Bilingual word embeddings have enabled cross-lingual transfer with small amounts of parallel data [1]}, [2]} or even none at all [3]}, [4]}, and are now used as features for state-of-the-art document-level [5]}, sentence-level [6]}, and targeted [7]} cross-lingual sentiment analysis approaches. The objective of bilingual embeddings is to learn a shared vector space in which translation pairs have similar vector representations. This benefits under-resourced languages as a sentiment classification model trained on the source-language can be applied directly to target-language data, without the need to translate it.
